Selection Bias - Revision history

Wiki Gardener at 15:54, 6 October 2014

2014-10-06T15:54:12Z

Wiki Gardener at 15:52, 6 October 2014

2014-10-06T15:52:49Z

Fungai Cecilia Sandamu at 14:20, 23 May 2014

2014-05-23T14:20:12Z

Katharina Wiedemann at 10:18, 17 May 2012

2012-05-17T10:18:05Z

Rauwol mar at 12:15, 2 June 2010

2010-06-02T12:15:25Z

Robert at 14:44, 27 January 2010

2010-01-27T14:44:31Z

Intern at 10:47, 17 November 2009

2009-11-17T10:47:36Z

Intern at 12:59, 3 November 2009

2009-11-03T12:59:46Z

Intern: New page: Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting c...

2009-11-03T12:59:03Z

New page: Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting c...

New page

Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an<o:p></o:p>

experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.

<o:p></o:p>

''Source:'' ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.''

@@ Line 2: / Line 2: @@
 = Overview =
-A '''selection bias''' is a statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. This may produce an inaccurate conclusion if the selection bias is not identified.<ref name="Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.html">Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.html
+A '''selection bias''' is a statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. This may produce an inaccurate conclusion if the selection bias is not identified.<ref name="Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.html">Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.htmlfckLR</ref>
 = Solutions =
-Randomized experiments solve the problem of selection bias by generating an experimental [[Control_Groups|control group]] of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In [[Quasi-Experimental_or_Non-Experimental_Designs|quasi-experimental designs]], statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.<ref name="Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)">Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)
+Randomized experiments solve the problem of selection bias by generating an experimental [[Control Groups|control group]] of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In [[Quasi-Experimental or Non-Experimental Designs|quasi-experimental designs]], statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.<ref name="Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)">Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)fckLR</ref>
 = Further Information<br/> =
 *[http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.]
+<br/>
 = References =
@@ Line 24: / Line 24: @@
 <references />
 [[Category:Questionnaires_/_Interviews]]

@@ Line 1: / Line 1: @@
-<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>
+= Overview =
-<br>
+A '''selection bias''' is a statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. This may produce an inaccurate conclusion if the selection bias is not identified.<ref name="Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.html">Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.html
 [[Category:Impacts]]

← Older revision		Revision as of 14:20, 23 May 2014
Line 8:		Line 8:

	[[Category:Impacts]]		[[Category:Impacts]]
		+	[[Category:Short_Pages]]

← Older revision		Revision as of 10:18, 17 May 2012
Line 1:		Line 1:
		+	{{Revision\|(PLEASE HELP TO INCORPORATE THIS ARTICLE INTO ANOTHER ONE).}}
		+
	<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>		<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>

← Older revision		Revision as of 12:15, 2 June 2010
Line 5:		Line 5:
	''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>		''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>

−	[[Category:~~Monitoring~~]]	+	[[Category:Impacts]]

← Older revision		Revision as of 14:44, 27 January 2010
Line 4:		Line 4:

	''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>		''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>
		+
		+	[[Category:Monitoring]]

← Older revision		Revision as of 12:59, 3 November 2009
Line 1:		Line 1:
−	<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an<~~o:p~~></o:p></font></span>	+	<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>

−	<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>

−	~~<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"><o:p></o:p></font></span>~~

	''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>		''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>